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Introduction The permissible exposure time to vibration for the operator is one of the key factors in maintaining the operator's health while optimizing machinery and equipment. The tractor studied was the ITM475, manufactured in Iran. The purpose of this study was to calculate the operator's permissible vibration exposure time while using the tractor to ensure the driver can maintain good bodily health. Materials and Methods In this study, experiments were conducted using a 3-axis vibration meter based on the ISO 2631 standard. The obtained data were analyzed through a factorial experiment using 18 treatments and 3 replications. The factors studied were engine rotation speed (at three levels of 1000, 1500, and 2000 rpm), road type (dirt and asphalt), and gear position (at three levels of 1, 2, and 3). Results and Discussion Various total vibration models were obtained for the tractor, and their determination coefficient varied from 90.11% for gear No. 3 on an asphalt road to 100% for gear No. 1 on an asphalt road and gear No. 2 on a dirt road. The maximum whole-body vibration, and consequently the minimum permissible exposure time, was observed for gear No. 3 at an engine rotation speed of 2000 rpm on a dirt road, which was 1.49 and 1.16 hours, respectively. Conclusion The maximum whole-body vibration experienced during an 8-hour tractor-driving session was measured at 0.85 m s -2 . It is important to note that the permissible exposure time decreases as vibration levels increase, and it reaches a limit of 1.16 hours. To ensure drivers adhere to these permissible exposure times across various driving conditions, measures must be implemented to reduce tractor vibration and minimize its transmission to the driver. By reducing overall tractor vibration and minimizing its impact on the driver, it becomes possible to increase the permissible exposure time for drivers.

Introduction The color of soil depends on its composition and this feature is easily available and rather stable. Fast and accurate determination of soil organic matter distribution in the agricultural fields is required, especially in precision farming. General laboratory methods for determining the soil organic carbon are expensive, time-consuming with many repetitions, and high consumption of chemicals. Soil scientists use the Munsell soil color diagrams to define the soil color. Due to the nature of Munsell color diagrams; this system is less suitable for recognizing exact color of the soil because of weak relationship and limited number of chips. Fast methods like image processing, colorimetric and spectroscopy provide a description of most physical characteristics of the soil color. Some of the advantages of using digital cameras was used in this study, are simple sampling of screened soil, being free from chemicals and toxic materials and being fast, inexpensive and precise. Materials and Methods In this research, 80 A-horizon (0-10 cm) soil samples were collected from various agricultural soils in west Azerbaijan, in the North West of Iran. Soil texture of these fields was loam clay and clay. The amount of organic carbon in samples was determined. The camera was installed at the distance of 0.5 m from the Petri dish on the lighting compartment. Captured images with the digital camera were saved in RGB color space. Processing operations were done by MATLAB 2012 software. The features extracted from the color images are used to model the soil organic carbon including the color features in different spaces. Four-color spaces including RGB, HSI, LAB and LUV were studied to find the relation between the color and the soil organic carbon. Results and Discussion The correlation of R component in the RGB model shows a strong single-parameter relation with the organic carbon as R 2 =0.83. This good relationship can be due to the compound information of the red color component on both brightness and chromaticity dimension. The results also show that the organic carbon has a relatively strong correlation with the light parameters, intensity and lightness in the HSI, Lab and LUV color spaces respectively. It also has a weak correlation with other parameters, since they cannot have a proper linear correlation with organic carbon due to their structural nature. Results show that the highest correlation is obtained when the R and G components participate in modeling and the component B is omitted. One explanation of this high correlation could be the high sensitivity of component B to the intensity and the angle of light. Even a small change in light changes this component. Thus, in order to reduce the effect of this component, it is better to omit it from the models and make models independent of it. In next section, 51 data were used to train neural network, 14 data were used to test the network and 12 data for network validating. The amount of soil organic carbon was output of the neural networks that was estimated after training using the color component values of each segment. To assess the accuracy of the network, estimated values and actual values of each sample were plotted in a graph. The minimum MSE values were 7.28e-6 with 16 neurons, 3.77e-6 with 14 neurons, 4.8e-3 with 10 neurons and 3.77e-6 with 12 neurons for RGB, HSI, Lab and LUV color spaces respectively. Conclusion The availability of digital cameras, possibility to use it in different situations, being inexpensive and providing many samples are the advantages of this method to estimate the soil organic carbon amount. Therefore, digital photography can be used as an analytical method to evaluate the soil fertility. It also requires a low cost of sample testing, and can provide a good possibility of time and place classification for studying a vast area. However to develop more reliable models, more effort is needed, such as collecting more soil samples of different areas that include a wide range of soil features.

The linear regression model is widely used in empirical work in Economics, Statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroskedasticity. Our results are obtained using high-dimensional approximations, where the number of included covariates are allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker-White heteroskedasticity consistent standard error estimators for linear models are inconsistent under this asymptotics. We then propose a new heteroskedasticity consistent standard error formula that is fully automatic and robust to both (conditional)\ heteroskedasticity of unknown form and the inclusion of possibly many covariates. We apply our findings to three settings: parametric linear models with many covariates, linear panel models with many fixed effects, and semiparametric semi-linear models with many technical regressors. Simulation evidence consistent with our theoretical results is also provided. The proposed methods are also illustrated with an empirical application.

More than thirty years ago Halbert White inaugurated a "model-robust" form of statistical inference based on the "sandwich estimator" of standard error. This estimator is known to be "heteroskedasticity-consistent", but it is less well-known to be "nonlinearity-consistent" as well. Nonlinearity, however, raises fundamental issues because regressors are no longer ancillary, hence can't be treated as fixed. The consequences are severe: (1)~the regressor distribution affects the slope parameters, and (2)~randomness of the regressors conspires with the nonlinearity to create sampling variability in slope estimates --- even in the complete absence of error. For these observations to make sense it is necessary to re-interpret population slopes and view them not as parameters in a generative model but as statistical functionals associated with OLS fitting as it applies to largely arbitrary joint $\xy$~distributions. In such a "model-robust" approach to linear regression, the meaning of slope parameters needs to be rethought and inference needs to be based on model-robust standard errors that can be estimated with sandwich plug-in estimators or with the $\xy$~bootstrap. Theoretically, model-robust and model-trusting standard errors can deviate by arbitrary magnitudes either way. In practice, a diagnostic test can be used to detect significant deviations on a per-slope basis.

Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physical processes of the data. In this article, and the companion papers of this series, we present the set of Generalized Linear Models (GLMs) as a fast alternative method for tackling general astronomical problems, including the ones related to the machine learning paradigm. To demonstrate the applicability of GLMs to inherently positive and continuous physical observables, we explore their use in estimating the photometric redshifts of galaxies from their multi-wavelength photometry. Using the gamma family with a log link function we predict redshifts from the PHoto-z Accuracy Testing simulated catalogue and a subset of the Sloan Digital Sky Survey from Data Release 10. We obtain fits that result in catastrophic outlier rates as low as ~1% for simulated and ~2% for real data. Moreover, we can easily obtain such levels of precision within a matter of seconds on a normal desktop computer and with training sets that contain merely thousands of galaxies. Our software is made publicly available as an user-friendly package developed in Python, R and via an interactive web application (https://cosmostatisticsinitiative.shinyapps.io/CosmoPhotoz). This software allows users to apply a set of GLMs to their own photometric catalogues and generates publication quality plots with minimum effort from the user. By facilitating their ease of use to the astronomical community, this paper series aims to make GLMs widely known and to encourage their implementation in future large-scale projects, such as the Large Synoptic Survey Telescope.

Penalized regression estimators are a popular tool for the analysis of sparse and high-dimensional data sets. However, penalized regression estimators defined using an unbounded loss function can be very sensitive to the presence of outlying observations, especially high leverage outliers. Moreover, it can be particularly challenging to detect outliers in high-dimensional data sets. Thus, robust estimators for sparse and high-dimensional linear regression models are in need. In this paper, we study the robust and asymptotic properties of MM-Bridge and adaptive MM-Bridge estimators: $\ell_q$-penalized MM-estimators of regression and MM-estimators with an adaptive $\ell_t$ penalty. For the case of a fixed number of covariates, we derive the asymptotic distribution of MM-Bridge estimators for all $q>0$. We prove that for $q

Penalized regression estimators are a popular tool for the analysis of sparse and high-dimensional data sets. However, penalized regression estimators defined using an unbounded loss function can be very sensitive to the presence of outlying observations, especially high leverage outliers. Moreover, it can be particularly challenging to detect outliers in high-dimensional data sets. Thus, robust estimators for sparse and high-dimensional linear regression models are in need. In this paper, we study the robust and asymptotic properties of MM-Bridge and adaptive MM-Bridge estimators: $\ell_q$-penalized MM-estimators of regression and MM-estimators with an adaptive $\ell_t$ penalty. For the case of a fixed number of covariates, we derive the asymptotic distribution of MM-Bridge estimators for all $q>0$. We prove that for $q

Penalized regression estimators are a popular tool for the analysis of sparse and high-dimensional data sets. However, penalized regression estimators defined using an unbounded loss function can be very sensitive to the presence of outlying observations, especially high leverage outliers. Moreover, it can be particularly challenging to detect outliers in high-dimensional data sets. Thus, robust estimators for sparse and high-dimensional linear regression models are in need. In this paper, we study the robust and asymptotic properties of MM-Bridge and adaptive MM-Bridge estimators: $\ell_q$-penalized MM-estimators of regression and MM-estimators with an adaptive $\ell_t$ penalty. For the case of a fixed number of covariates, we derive the asymptotic distribution of MM-Bridge estimators for all $q>0$. We prove that for $q

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A number of important recent advances in econometric theory are related to the methods of truncated regression model - the regression model in which the range of the dependent variable is restricted to some interval of (-infinity, infinity), usually the non-negative half-line, such as the income of an individual. Powell used the L sub 1-norm criterion with some modifications in estimating the regression coefficients in truncated linear models. He proved the consistency and asymptotic normality of his estimates under a set of conditions. On the other hand, Nawata's paper uses the ordinary L sub 2-norm (least square) criterion, along with a grouping and adjustment of the observed data. In his view, his method has the merit of easy computation compared with the method of Powell. This paper borrows the basic idea of Nawata in grouping and adjusting the observed data. But the authors make simplifications in the procedure of grouping, which enables us to make substantial extensions of the results of Nawata's paper under weakened conditions. Keywords: Linear median regression; Truncated regression; Parameters; Linearity.

A number of important recent advances in econometric theory are related to the methods of truncated regression model - the regression model in which the range of the dependent variable is restricted to some interval of (-infinity, infinity), usually the non-negative half-line, such as the income of an individual. Powell used the L sub 1-norm criterion with some modifications in estimating the regression coefficients in truncated linear models. He proved the consistency and asymptotic normality of his estimates under a set of conditions. On the other hand, Nawata's paper uses the ordinary L sub 2-norm (least square) criterion, along with a grouping and adjustment of the observed data. In his view, his method has the merit of easy computation compared with the method of Powell. This paper borrows the basic idea of Nawata in grouping and adjusting the observed data. But the authors make simplifications in the procedure of grouping, which enables us to make substantial extensions of the results of Nawata's paper under weakened conditions. Keywords: Linear median regression; Truncated regression; Parameters; Linearity.

This document considers a linear regression model, where (x sub i) is a sequence of experimental points, i. e., known p-vectors, (e sub i) is a sequence of independent random errors, with med(e sub i) =0,i= 1,2....Define the minimum L1 -norm estimate of (alpha, beta)', by (alpha, beta)', to be chosen such that under quite general conditions on (x sub i) and (e sub i), the strong consistency of the minimum L1 -norm estimate is established. Further, under an additional condition on (x sub i), it is also proved that for any given epsilon 0, there exist constant C O not depending on n.

This paper deals with the issue of testing hypothesis in symmetric and log-symmetric linear regression models in small and moderate-sized samples. We focus on four tests, namely the Wald, likelihood ratio, score, and gradient tests. These tests rely on asymptotic results and are unreliable when the sample size is not large enough to guarantee a good agreement between the exact distribution of the test statistic and the corresponding chi-squared asymptotic distribution. Bartlett and Bartlett-type corrections typically attenuate the size distortion of the tests. These corrections are available in the literature for the likelihood ratio and score tests in symmetric linear regression models. Here, we derive a Bartlett-type correction for the gradient test. We show that the corrections are also valid for the log-symmetric linear regression models. We numerically compare the various tests, and bootstrapped tests, through simulations. Our results suggest that the corrected and bootstrapped tests exhibit type I probability error closer to the chosen nominal level with virtually no power loss. The analytically corrected tests, including the Bartlett-corrected gradient test derived in this paper, perform as well as the bootstrapped tests with the advantage of not requiring computationally-intensive calculations. We present two real data applications to illustrate the usefulness of the modified tests. Keywords: Symmetric regression models; Bartlett correction; Bartlett-type correction; Bootstrap; Log-symmetric regression models; gradient statistic; score statistic; likelihood ratio statistic; Wald statistic.

In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problems, and the magnitude of their effects tapers off. It is reasonable to model the number of relevant features as a diverging sequence when sample size increases. In this article, we investigate the properties of the extended Bayes information criterion (EBIC) (Chen and Chen, 2008) for feature selection in linear regression models with diverging number of relevant features in high or ultra-high dimensional feature spaces. The selection consistency of the EBIC in this situation is established. The application of EBIC to feature selection is considered in a two-stage feature selection procedure. Simulation studies are conducted to demonstrate the performance of the EBIC together with the two-stage feature selection procedure in finite sample cases.

In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular cluster population $N_{\rm GC}$ is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between $N_{\rm GC}$ and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion, and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous), and allows modelling the population of globular clusters on their natural scale as a non-negative integer variable. Prediction intervals of 99% around the trend for expected $N_{\rm GC}$comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35% smaller than other types with similar brightness.

This paper considers estimation of the predictive density for a normal linear model with unknown variance under alpha-divergence loss for -1

Despite the broad appeal of missing data handling approaches that assume a missing at random (MAR) mechanism (e.g., multiple imputation and maximum likelihood estimation), some very common analysis models in the behavioral science literature are known to cause bias-inducing problems for these approaches. Regression models with incomplete interactive or polynomial effects are a particularly important example because they are among the most common analyses in behavioral science research applications. In the context of single-level regression, fully Bayesian (model-based) imputation approaches have shown great promise with these popular analysis models. The purpose of this paper is to extend model-based imputation to multilevel models with up to three levels, including functionality for mixtures of categorical and continuous variables. Computer simulation results suggest that this new approach can be quite effective when applied to multilevel models with random coefficients and interaction effects. In most scenarios that we examined, imputation-based parameter estimates were quite accurate and tracked closely with those of the complete data. The new procedure is available in the Blimp software application for macOS, Windows, and Linux, and the paper includes a data analysis example illustrating its use. [This is the online version of an article published in "Psychological Methods."]

We propose to address the common problem of linear estimation in linear statistical models by using a model selection approach via penalization. Depending then on the framework in which the linear statistical model is considered namely the regression framework or the inverse problem framework, a data-driven model selection criterion is obtained either under general assumptions, or under the mild assumption of model identifiability respectively. The proposed approach was stimulated by the important recent non-asymptotic model selection results due to Birg\'e and Massart mainly (Birge and Massart 2007), and our results in this paper, like theirs, are non-asymptotic and turn to be sharp. Our main contribution in this paper resides in the fact that these linear estimators are not necessarily least-squares estimators but can be any linear estimators. The proposed approach finds therefore potential applications in countless fields of engineering and applied science (image science, signal processing,applied statistics, coding, to name a few) in which one is interested in recovering some unknown vector quantity of interest as the one, for example, which achieves the best trade-off between a term of fidelity to data, and a term of regularity or/and parsimony of the solution. The proposed approach provides then such applications with an interesting model selection framework that allows them to achieve such a goal.

We propose to address the common problem of linear estimation in linear statistical models by using a model selection approach via penalization. Depending then on the framework in which the linear statistical model is considered namely the regression framework or the inverse problem framework, a data-driven model selection criterion is obtained either under general assumptions, or under the mild assumption of model identifiability respectively. The proposed approach was stimulated by the important recent non-asymptotic model selection results due to Birg\'e and Massart mainly (Birge and Massart 2007), and our results in this paper, like theirs, are non-asymptotic and turn to be sharp. Our main contribution in this paper resides in the fact that these linear estimators are not necessarily least-squares estimators but can be any linear estimators. The proposed approach finds therefore potential applications in countless fields of engineering and applied science (image science, signal processing,applied statistics, coding, to name a few) in which one is interested in recovering some unknown vector quantity of interest as the one, for example, which achieves the best trade-off between a term of fidelity to data, and a term of regularity or/and parsimony of the solution. The proposed approach provides then such applications with an interesting model selection framework that allows them to achieve such a goal.

We propose to address the common problem of linear estimation in linear statistical models by using a model selection approach via penalization. Depending then on the framework in which the linear statistical model is considered namely the regression framework or the inverse problem framework, a data-driven model selection criterion is obtained either under general assumptions, or under the mild assumption of model identifiability respectively. The proposed approach was stimulated by the important recent non-asymptotic model selection results due to Birg\'e and Massart mainly (Birge and Massart 2007), and our results in this paper, like theirs, are non-asymptotic and turn to be sharp. Our main contribution in this paper resides in the fact that these linear estimators are not necessarily least-squares estimators but can be any linear estimators. The proposed approach finds therefore potential applications in countless fields of engineering and applied science (image science, signal processing,applied statistics, coding, to name a few) in which one is interested in recovering some unknown vector quantity of interest as the one, for example, which achieves the best trade-off between a term of fidelity to data, and a term of regularity or/and parsimony of the solution. The proposed approach provides then such applications with an interesting model selection framework that allows them to achieve such a goal.

This paper investigates correct variable selection in finite samples via $\ell_1$ and $\ell_1+\ell_2$ type penalization schemes. The asymptotic consistency of variable selection immediately follows from this analysis. We focus on logistic and linear regression models. The following questions are central to our paper: given a level of confidence $1-\delta$, under which assumptions on the design matrix, for which strength of the signal and for what values of the tuning parameters can we identify the true model at the given level of confidence? Formally, if $\widehat{I}$ is an estimate of the true variable set $I^*$, we study conditions under which $\mathbb{P}(\widehat{I}=I^*)\geq 1-\delta$, for a given sample size $n$, number of parameters $M$ and confidence $1-\delta$. We show that in identifiable models, both methods can recover coefficients of size $\frac{1}{\sqrt{n}}$, up to small multiplicative constants and logarithmic factors in $M$ and $\frac{1}{\delta}$. The advantage of the $\ell_1+\ell_2$ penalization over the $\ell_1$ is minor for the variable selection problem, for the models we consider here. Whereas the former estimates are unique, and become more stable for highly correlated data matrices as one increases the tuning parameter of the $\ell_2$ part, too large an increase in this parameter value may preclude variable selection.

Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group selection. Nonconvex penalties such as SCAD and MCP have been proposed and shown to have several advantages over the lasso; these penalties may also be extended to the group selection problem, giving rise to group SCAD and group MCP methods. Here, we describe algorithms for fitting these models stably and efficiently. In addition, we present simulation results and real data examples comparing and contrasting the statistical properties of these methods.

The study investigates the applicability of linear regression and ANN models for estimating weekly reference evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh. The climatic parameters influencing ET0 were identified through multiple and partial correlation analysis. The sunshine, temperature, wind velocity and relative humidity mostly influenced the study area in the weekly ET0 estimation. Linear regression models in terms of the climatic parameters influencing the regions and, optimal neural network architectures considering these climatic parameters as inputs were developed. The models’ performance was evaluated with respect to ET0 estimated by FAO-56 Penman-Monteith method. The linear regression models showed a satisfactory performance in the weekly ET0 estimation in the regions selected for the present study. The ANN (4,4,1) models, however, consistently showed a slightly improved performance over linear regression models.

The study investigates the applicability of linear regression and ANN models for estimating weekly reference evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh. The climatic parameters influencing ET0 were identified through multiple and partial correlation analysis. The sunshine, temperature, wind velocity and relative humidity mostly influenced the study area in the weekly ET0 estimation. Linear regression models in terms of the climatic parameters influencing the regions and, optimal neural network architectures considering these climatic parameters as inputs were developed. The models’ performance was evaluated with respect to ET0 estimated by FAO-56 Penman-Monteith method. The linear regression models showed a satisfactory performance in the weekly ET0 estimation in the regions selected for the present study. The ANN (4,4,1) models, however, consistently showed a slightly improved performance over linear regression models.

The study investigates the applicability of linear regression and ANN models for estimating weekly reference evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh. The climatic parameters influencing ET 0 were identified through multiple and partial correlation analysis. The sunshine, temperature, wind velocity and relative humidity mostly influenced the study area in the weekly ET0 estimation. Linear regression models in terms of the climatic parameters influencing the regions and, optimal neural network architectures considering these climatic parameters as inputs were developed. The models’ performance was evaluated with respect to ET0 estimated by FAO-56 Penman-Monteith method. The linear regression models showed a satisfactory performance in the weekly ET 0 estimation in the regions selected for the present study. The ANN (4,4,1) models, however, consistently showed a slightly improved performance over linear regression models.

The study investigates the applicability of linear regression and ANN models for estimating weekly reference evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh. The climatic parameters influencing ET 0 were identified through multiple and partial correlation analysis. The sunshine, temperature, wind velocity and relative humidity mostly influenced the study area in the weekly ET0 estimation. Linear regression models in terms of the climatic parameters influencing the regions and, optimal neural network architectures considering these climatic parameters as inputs were developed. The models’ performance was evaluated with respect to ET0 estimated by FAO-56 Penman-Monteith method. The linear regression models showed a satisfactory performance in the weekly ET 0 estimation in the regions selected for the present study. The ANN (4,4,1) models, however, consistently showed a slightly improved performance over linear regression models.

The study investigates the applicability of linear regression and ANN models for estimating weekly reference evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh. The climatic parameters influencing ET 0 were identified through multiple and partial correlation analysis. The sunshine, temperature, wind velocity and relative humidity mostly influenced the study area in the weekly ET0 estimation. Linear regression models in terms of the climatic parameters influencing the regions and, optimal neural network architectures considering these climatic parameters as inputs were developed. The models’ performance was evaluated with respect to ET0 estimated by FAO-56 Penman-Monteith method. The linear regression models showed a satisfactory performance in the weekly ET 0 estimation in the regions selected for the present study. The ANN (4,4,1) models, however, consistently showed a slightly improved performance over linear regression models.

The study investigates the applicability of linear regression and ANN models for estimating weekly reference evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh. The climatic parameters influencing ET 0 were identified through multiple and partial correlation analysis. The sunshine, temperature, wind velocity and relative humidity mostly influenced the study area in the weekly ET0 estimation. Linear regression models in terms of the climatic parameters influencing the regions and, optimal neural network architectures considering these climatic parameters as inputs were developed. The models’ performance was evaluated with respect to ET0 estimated by FAO-56 Penman-Monteith method. The linear regression models showed a satisfactory performance in the weekly ET 0 estimation in the regions selected for the present study. The ANN (4,4,1) models, however, consistently showed a slightly improved performance over linear regression models.

Includes bibliographical references (p. 17)

This paper investigates the effectiveness of traditional machine learning techniques, namely linear regression and random forest, in enhancing the detection of money laundering (ML) activities within financial systems. As ML schemes evolve in complexity, traditional rule-based methods struggle with high false favorable rates and a lack of adaptability, prompting the need for more sophisticated analytical approaches. In contrast to the complexities of deep learning models, this study explores the potential of these more accessible machine learning methods in identifying and analyzing suspicious transactional patterns. We apply linear regression and random forest (RF) models to transactional data to detect anomalous activities that could indicate ML. Our research thoroughly compares these models based on key performance metrics such as accuracy, precision, and recall. The findings suggest that while less complex than deep learning frameworks, linear regression, and RF models offer substantial benefits. They provide a more streamlined, interpretable, and efficient alternative to conventional rule-based systems in the context of ML detection. This study contributes to the ongoing discourse on the application of machine learning in financial crime detection, demonstrating the practicality and effectiveness of these methods in a critical area of financial security.

This paper investigates the effectiveness of traditional machine learning techniques, namely linear regression and random forest, in enhancing the detection of money laundering (ML) activities within financial systems. As ML schemes evolve in complexity, traditional rule-based methods struggle with high false favorable rates and a lack of adaptability, prompting the need for more sophisticated analytical approaches. In contrast to the complexities of deep learning models, this study explores the potential of these more accessible machine learning methods in identifying and analyzing suspicious transactional patterns. We apply linear regression and random forest (RF) models to transactional data to detect anomalous activities that could indicate ML. Our research thoroughly compares these models based on key performance metrics such as accuracy, precision, and recall. The findings suggest that while less complex than deep learning frameworks, linear regression, and RF models offer substantial benefits. They provide a more streamlined, interpretable, and efficient alternative to conventional rule-based systems in the context of ML detection. This study contributes to the ongoing discourse on the application of machine learning in financial crime detection, demonstrating the practicality and effectiveness of these methods in a critical area of financial security.

Five issues relative to the use of different Ordinary Least Squares (OLS) and Hierarchical Linear Modeling (HLM) models to identify effective schools and teachers were examined using data from all students in the Dallas (Texas) public schools in grade 3 in 1994 and grade 4 in 1995. OLS models using first- and second-order interactions produced results that were very close to those produced by two-level HLM models at the school level and two- and three-level HLM models at the teacher level. Most OLS regression and HLM models used in this study accounted for more than 70% of the variance in student achievement in reading and mathematics. Results produced by all the models were extremely consistent, and correlations produced by the various models were all generally above 0.90. Correlations of results with important school, teacher, and student-level contextual variables were negligible for all models, meaning that the various models produced results that were free from bias relative to important contextual variables. Correlations of results with prescore characteristics were negligible for all models, meaning that the various models produced results that were free from bias relative to the level of pretest scores. Taking all results into consideration, it is recommended that a two-level HLM model (student-school) be implemented to determine school effect, and that the empirical Bayes residuals from the model be adjusted with an adjustment for shrinkage to form the basis for estimates of teacher effect. Appropriate formulas for this task are included. (Contains 34 references.) (SLD)

In the U.S., there are currently 13.7 million cancer survivors (38). Many cancer survivors experience problems with post-treatment mental and physical functioning.Although research has identified important contributing factors regarding these problems, traditional predictive statistical modeling accounts for less than half the variance in mental and physical function (16; 17; 113). The relationship among these factors may be better accounted for by a non-linear modeling approach. The goal of this doctoral study was to determine whether a non-linear, adaptive predictive model demonstrated better model fit, showed greater predictive accuracy, and accounted for a greater contribution of independent variables over a traditional statistical model with regard to mental and physical functioning in post-treatment breast cancer survivors. Using demographic, medical, and clinical variables, linear regression was compared to neural network modeling in predicting mental functioning and physical functioning in a sample of post-treatment breast cancer survivors. Contrary to the a priori hypotheses, the neural network model did not outperform the linear regression model in predicting mental and physical functioning of post-treatment breast cancer survivors. However, both linear regression and neural network modeling identified modifiable variables (clinical domains of the Cancer Survivor Profile) as important predictors of post-treatment mental and physical functioning, with the neural network confirming the findings of the linear regression models. The neural network model also added to the results of the linear regression by identifying additional important variables (age, time since diagnosis) that may have a non-linear relationship with mental and physical functioning. These findings may promote a better understanding of post-treatment health status and promote targeted clinical interventions.

In the U.S., there are currently 13.7 million cancer survivors (38). Many cancer survivors experience problems with post-treatment mental and physical functioning.Although research has identified important contributing factors regarding these problems, traditional predictive statistical modeling accounts for less than half the variance in mental and physical function (16; 17; 113). The relationship among these factors may be better accounted for by a non-linear modeling approach. The goal of this doctoral study was to determine whether a non-linear, adaptive predictive model demonstrated better model fit, showed greater predictive accuracy, and accounted for a greater contribution of independent variables over a traditional statistical model with regard to mental and physical functioning in post-treatment breast cancer survivors. Using demographic, medical, and clinical variables, linear regression was compared to neural network modeling in predicting mental functioning and physical functioning in a sample of post-treatment breast cancer survivors. Contrary to the a priori hypotheses, the neural network model did not outperform the linear regression model in predicting mental and physical functioning of post-treatment breast cancer survivors. However, both linear regression and neural network modeling identified modifiable variables (clinical domains of the Cancer Survivor Profile) as important predictors of post-treatment mental and physical functioning, with the neural network confirming the findings of the linear regression models. The neural network model also added to the results of the linear regression by identifying additional important variables (age, time since diagnosis) that may have a non-linear relationship with mental and physical functioning. These findings may promote a better understanding of post-treatment health status and promote targeted clinical interventions.

This article is from G3: Genes|Genomes|Genetics , volume 2 . Abstract In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Includes bibliographies and index

Includes bibliographies and index

Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide powerful generalizations of linear regression, where the target variable is assumed to be a (possibly unknown) 1-dimensional function of a linear predictor. In general, these problems entail non-convex estimation procedures, and, in practice, iterative local search heuristics are often used. Kalai and Sastry (2009) recently provided the first provably efficient method for learning SIMs and GLMs, under the assumptions that the data are in fact generated under a GLM and under certain monotonicity and Lipschitz constraints. However, to obtain provable performance, the method requires a fresh sample every iteration. In this paper, we provide algorithms for learning GLMs and SIMs, which are both computationally and statistically efficient. We also provide an empirical study, demonstrating their feasibility in practice.

Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide powerful generalizations of linear regression, where the target variable is assumed to be a (possibly unknown) 1-dimensional function of a linear predictor. In general, these problems entail non-convex estimation procedures, and, in practice, iterative local search heuristics are often used. Kalai and Sastry (2009) recently provided the first provably efficient method for learning SIMs and GLMs, under the assumptions that the data are in fact generated under a GLM and under certain monotonicity and Lipschitz constraints. However, to obtain provable performance, the method requires a fresh sample every iteration. In this paper, we provide algorithms for learning GLMs and SIMs, which are both computationally and statistically efficient. We also provide an empirical study, demonstrating their feasibility in practice.